Aerial helicopter target detection and identification method based on micro-Doppler effect
Through the Micro Doppler -based air helicopter target detection and identification method, the problem of poor hover target recognition effects and large calculation quantities were solved, and the accurate identification and engineering realization of exercise and suspension targets were realized.
Patent Information
- Application Number
- CN202411700067.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-26
- Publication Date
- 2025-01-28
AI Technical Summary
The prior art has poor results in identifying hover targets and is computationally large, making it difficult to achieve engineering.
Using the air helicopter target detection and identification method based on the micro -Doppler effect, by obtaining the radar back pulse pressure data, MTD processing, CFAR detection and point condensing, determine the potential movement target points, and through the first distance Doppler Doppler Doppler The distribution of the plane CFAR point, find the potential hover target point, and finally recognize it.
A stable and accurate identification movement goals and suspension targets have been achieved. The identification results are only related to the Doppler spectrum of the target. There is no need for data training, small calculation amount, and easy to achieve engineering.
Smart Images
Figure CN119375853A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of target recognition, and in particular to a method for detecting and recognizing aerial helicopter targets based on the micro-Doppler effect. Background Art
[0002] Moving targets produce Doppler frequency shift in radar echoes, which is the Doppler effect. If the target has rotating or oscillating structural components in the beam line of sight, these moving components will cause additional frequency modulation around the target center frequency, which is called the micro-Doppler effect. When detecting moving targets in the air, the classic moving target detection method can be used to detect them well, but for hovering targets, their speed is basically zero and they are located in the clutter area in the radar echo. In this case, the classic moving target detection method cannot be used to detect them well. At present, many methods use methods such as support vector machines (SVM) and convolutional neural networks (CNN) to identify hovering targets, but this method requires weight learning and training through target data. The recognition effect is strongly related to the data used for training, and the amount of calculation is large, which is not conducive to engineering implementation. Summary of the invention
[0003] The main purpose of this application is to provide an aerial helicopter target detection and recognition method based on the micro-Doppler effect, aiming at solving the problem that the recognition effect of the existing detection method is related to the data used for training, resulting in a large amount of calculation.
[0004] To achieve the above-mentioned purpose, the present application provides an aerial helicopter target detection and identification method based on micro-Doppler effect, comprising: obtaining radar echo pulse pressure data, performing MTD processing on the echo pulse pressure data, and obtaining a first range Doppler plane; performing CFAR detection on the first range Doppler plane data, and respectively obtaining a moving target point and a slightly moving target point; obtaining the main-side lobe ratio of the slightly moving target point, and inserting the slightly moving target point whose main-side lobe ratio meets a preset condition into the moving target point to obtain the target point; obtaining a target area on the first range Doppler plane according to the target point; performing MTD processing on the echo pulse pressure data of the target area to obtain a second range Doppler plane; performing non-coherent superposition processing on the second range Doppler plane to obtain a third range Doppler plane, and performing CFAR detection on the point traces on the third range Doppler plane to obtain a second CFAR detection point trace; in the range unit for determining the target point, when there is a range unit whose number of second CFAR detection point traces is greater than a second preset point trace number threshold, determining the main-side lobe ratio of the target point, and determining the target point whose main-side lobe ratio meets the preset condition as the final target.
[0005] Optionally, the target distance unit is determined according to the number of dots in each distance unit, and the micro-motion target point is obtained in the target distance unit, including: if the number of dots in each distance unit in multiple consecutive distance units is greater than a preset dot number threshold, the distance unit with the largest number of dots is taken as the target distance unit; the dot in the Doppler channel corresponding to the position with the strongest amplitude in the target distance unit is taken as the micro-motion target point.
[0006] Optionally, the target area is acquired by: acquiring the entire azimuth dimension area centered on the range unit of the target point on the first range Doppler plane as a potential target area; determining the signal-to-noise ratio of the target point, and when it is determined that the signal-to-noise ratio of the target point is greater than a preset signal-to-noise ratio threshold, determining the potential target area as the target area.
[0007] Optionally, the signal-to-noise ratio determines the signal-to-noise ratio based on the amplitude of the target point and the average amplitude of all points in the potential target area.
[0008] Optionally, the second range Doppler plane is subjected to non-coherent superposition processing to obtain a third range Doppler plane, including: dividing the second range Doppler plane into a first plane and a second plane according to the Doppler channel; adding the point amplitude in each Doppler channel in the first plane with the point amplitude in the corresponding Doppler channel in the second plane to obtain the third range Doppler plane; wherein the sum of all the two combined Doppler channel numbers is the same.
[0009] Optionally, before performing MTD processing on the echo pulse pressure data of the target area, the method further includes: performing clutter removal processing on the echo pulse pressure data of the target area.
[0010] Optionally, the echo pulse pressure data of the target area is processed by using a Clean algorithm to remove clutter.
[0011] Optionally, the preset condition is that the main-to-side lobe ratio does not satisfy the main-to-side lobe attenuation performance of the Hamming window.
[0012] Compared with the prior art, the present invention has the following beneficial effects: The method for detecting and identifying aerial helicopter targets based on the micro-Doppler effect of the present invention determines potential moving target points by performing MTD processing, CFAR detection, and point trace aggregation on the collected radar echo pulse pressure data, and then finds potential hovering target points according to the distribution of CFAR point traces in the first distance Doppler plane. Finally, the potential moving target points and the potential hovering target points are uniformly identified, so that not only the moving target can be stably and accurately identified, but also the hovering target can be accurately identified. The identification result is only related to the Doppler spectrum of the target, and no data training is required. The implementation is simple, the calculation amount is small, and it is easy to realize engineering. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Figure 1 A flowchart of a method for detecting and identifying aerial helicopter targets based on the micro-Doppler effect is provided for this application; Figure 2 This is a detection result diagram of the first distance Doppler plane obtained by a method for detecting and identifying aerial helicopter targets based on the micro-Doppler effect in this application; Figure 3 This is a flowchart of hovering target detection in a method for detecting and identifying aerial helicopter targets based on micro-Doppler effect in this application; Figure 4 This is a result diagram of the number of Doppler dimension traces exceeding the threshold in a method for detecting and identifying aerial helicopter targets based on the micro-Doppler effect in this application; Figure 5 This is a comparison diagram of the main lobe and side lobe ratios of a moving target point and a slightly moving target point in a method for detecting and identifying aerial helicopter targets based on the micro-Doppler effect in this application; Figure 6 This is a detection result diagram of the first distance Doppler plane after inserting the target point in the aerial helicopter target detection and recognition method based on the micro-Doppler effect of this application; Figure 7 This is a target recognition processing flow chart of a method for detecting and identifying aerial helicopter targets based on micro-Doppler effect in this application; Figure 8 This is a detection result diagram of the second range Doppler plane obtained by a method for detecting and identifying aerial helicopter targets based on the micro-Doppler effect in this application; Fig. 9 This is a diagram of detection and recognition results in a method for detecting and recognizing aerial helicopter targets based on the micro-Doppler effect in this application.
[0014] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION
[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0016] The first embodiment of the present invention provides a method for detecting and identifying aerial helicopter targets based on the micro-Doppler effect. Figure 1 As shown, the specific steps include: Example 1 Step S1, obtain the radar echo pulse pressure data, perform MTD (Moving Targets Detection) processing on the echo pulse pressure data, and obtain the first range Doppler plane, see Figure 2 ; Perform CFAR (Constant False Alarm Rate) processing on the point traces on the range Doppler plane to obtain the first CFAR point traces; perform point trace aggregation on the first CFAR point traces to obtain the moving target point; In this embodiment, the target point is a helicopter. Since the speed of a hovering helicopter is approximately 0 and it is located in a clutter area, it cannot be detected by step S1. However, its micro-Doppler effect still exists, and multiple points will be detected in the Doppler dimension where the helicopter target is located. Therefore, the moving target points should be eliminated in the first distance Doppler plane, and the hovering helicopter should be detected. The details are as follows: Figure 3 shown.
[0017] Step 2: obtaining the number of points in each range unit except the range unit of the moving target point on the first range Doppler plane; determining the target range unit according to the number of points in each range unit, and obtaining the micro-moving target point in the target range unit; Specifically, step 21, obtaining the number of points in each distance unit except the distance unit of the moving target point on the first range Doppler plane; that is, eliminating the distance unit containing the moving target point; Step 22: if the number of points in each of the multiple consecutive distance units is greater than the first preset point threshold, the distance unit with the largest number of points is taken as the target distance unit; Among them, since the target is extended in distance, the micro-Doppler effect of the helicopter target will exist in three consecutive distance units. If the number of points in each distance unit in the three consecutive distance units is greater than the first preset point threshold, it can be confirmed as the target distance unit. The first preset point threshold can be set according to actual needs. The results after screening by the first preset point threshold are as follows Figure 4 shown.
[0018] Step 23: The point trace in the Doppler channel corresponding to the position with the strongest amplitude in the target range unit is taken as the micro-motion target point.
[0019] Step 3, obtaining the main lobe to side lobe ratio of the micro-motion target point, inserting the micro-motion target point whose main lobe to side lobe ratio meets the preset conditions into the moving target point to obtain the target point; Specifically, since a Hamming window is added in the MTD processing to reduce the side lobes of the target, the main lobe to side lobe ratio of the target satisfies the main lobe to side lobe attenuation performance of the Hamming window, while the micro-Doppler effect of the helicopter does not meet this condition. The comparison results of the main lobe to side lobe ratio of the helicopter and the strong static target are as follows: Figure 5 If the micro-motion target point is very strong, its side lobe will also be detected by CFAR. Therefore, this embodiment obtains the target point in the following way.
[0020] Determine the main-sidelobe ratio of the micro-moving target point, and insert the micro-moving target point whose main-sidelobe ratio does not meet the main-sidelobe attenuation performance of the Hamming window into the moving target point according to the order of Doppler and distance, that is, insert the selected micro-moving target point into the first distance Doppler plane, and obtain the target point in the first distance Doppler plane, such as Figure 6 If the main lobe to side lobe ratio of the micro-motion target point meets the main lobe to side lobe attenuation performance of the Hamming window, the target point is eliminated.
[0021] Step 4: Acquire the target area on the first range Doppler plane according to the target point; perform MTD processing on the echo pulse pressure data of the target area to obtain a second range Doppler plane, perform non-coherent superposition processing on the second range Doppler plane to obtain a third range Doppler plane; specifically, Figure 7 shown.
[0022] Step S41, obtaining the entire azimuth dimension area centered on the range unit of the target point on the first range Doppler plane as a potential target area; Step S42, determining the signal-to-noise ratio of the target point in the potential target area, and when it is determined that the signal-to-noise ratio of the target point is greater than a preset signal-to-noise ratio threshold, determining the potential target area as the target area; wherein the signal-to-noise ratio is determined according to the amplitude of the target point and the average amplitude of all points in the potential target area; In this embodiment, due to the micro-Doppler effect of the helicopter target, the signal-to-noise ratio of the target area is high. Therefore, if the signal-to-noise ratio of the target area is greater than the threshold, it is considered that there is a helicopter target in the area and identification is performed. Otherwise, it is considered that there is no helicopter target and helicopter target identification is not performed. Step S42: Clean algorithm performs clutter removal processing on the echo pulse pressure data of the target area, and then performs MTD processing on the processed echo pulse pressure data to obtain a second range Doppler plane.
[0023] In order to enhance the amplitude of the Doppler channel, this embodiment performs the following processing on the second range Doppler plane.
[0024] Step S43, dividing the second range Doppler plane into a first plane and a second plane according to the Doppler channel; merging the point traces in each Doppler channel in the first plane with the point traces in the corresponding Doppler channel in the second plane to obtain a third range Doppler plane; wherein the sum of all the two merged Doppler channel numbers is the same.
[0025] Step 5: Perform CFAR detection on the points on the third range Doppler plane to obtain the second CFAR detection points, such as Figure 8 As shown; determine the maximum value of the number of second CFAR detection points in each distance unit of the target point, and record the maximum value as TarDotmax.
[0026] Step 7: When there is a distance unit in which the number of second CFAR detection traces is greater than the second preset trace threshold in the distance unit for determining the target point, determine the main-side lobe ratio of the target point, and determine the target point whose main-side lobe ratio meets the preset conditions as the final target.
[0027] In this embodiment, when it is determined that the TarDotmax value is greater than the second preset point trace threshold, it is considered that the target may be a helicopter and subsequent processing is performed, otherwise the target is considered not to be a helicopter; the main lobe to side lobe ratio of the target is used to eliminate false alarms caused by the side lobes of strong static targets; if the main lobe to side lobe ratio of the target satisfies the main lobe to side lobe attenuation performance of the Hamming window, the target is considered to be a helicopter, otherwise it is not a helicopter, and the recognition result is as follows: Fig. 9 shown.
[0028] Example 2 Step S1, obtain the radar echo pulse pressure data, perform MTD (Moving Targets Detection) processing on the echo pulse pressure data, and obtain the first range Doppler plane, see Figure 2 ; Perform CFAR (Constant False Alarm Rate) processing on the range Doppler data along the range dimension to obtain the traces that meet the signal-to-noise ratio amplitude threshold after CFAR processing; perform point condensation on the traces after CFAR to obtain the moving target point; In this embodiment, the detection and identification object is a helicopter. Since the speed of a hovering helicopter is approximately 0 and it is located in a clutter area with a strong signal amplitude, the signal reflected by the helicopter body cannot be detected by step S1, but its micro-Doppler effect still exists, and multiple points are detected in the longitudinal Doppler dimension of the distance unit where the helicopter target is located. The distribution of the CFAR points in the first range Doppler plane in the distance dimension is counted, and the influence of the points that are clearly moving targets is eliminated. The micro-movement target points caused by micro-movement features such as the rotor are retained to detect the hovering helicopter, as follows: Figure 3 shown.
[0029] Step 2: Obtain the distance and Doppler information of the CFAR detection point traces in the first range Doppler plane, and count the number of CFAR detection point traces in the longitudinal Doppler dimension in each range unit except the distance of the moving target point; determine the target range unit according to the number of Doppler dimension traces in each range unit, and obtain the micro-motion target point in the target range unit.
[0030] Specifically, step 21, obtain the distance and Doppler information of the CFAR detection point traces in the first range Doppler plane, and count the number of CFAR detection point traces in the longitudinal Doppler dimension in each range unit except the distance of the moving target point; by setting a threshold, eliminate the range unit of the moving target point with typical no micro-motion characteristics, and the range unit with the number of traces exceeding the threshold may have potential helicopter targets; Step 22: The system uses high range resolution parameters and range window function characteristics, which will cause the target to be extended in distance. The micro-Doppler effect of the helicopter target will exist in multiple continuous range units. Therefore, if there are continuous range units that meet the threshold of step 21, since the center of the helicopter is the range unit with the strongest micro-Doppler effect, the range unit with the largest number of continuous distances and the number of points exceeding the threshold is selected, and the range unit with the largest number of points is taken as the target range unit. The result after the point number threshold screening is as follows: Figure 4 As shown; Step 23. After obtaining the range unit of the suspected helicopter target in step 22, the detection of the helicopter target is realized through the rotor component. In order to obtain the Doppler information of the target, the amplitudes of all Doppler channels of the range unit are traversed, and the index value of the point with the maximum amplitude is selected as the Doppler channel number information of the target.
[0031] Step 3, in order to prevent the detected target information from being a stationary target due to strong reflection, according to the Doppler main-sidelobe ratio characteristics of the strong stationary target, the target point of step 23 whose Doppler main-sidelobe ratio meets the preset conditions is inserted into the point trace list confirmed as the target, and is merged with the moving target point with typical no micro-motion characteristics eliminated in step 21 as the confirmed target point trace; specifically, since the data will be windowed during the MTD processing process to reduce the Doppler sidelobe of the target, and the window characteristics will ultimately be reflected in the Doppler main-sidelobe attenuation performance of the strong stationary target, and the target of the helicopter with micro-Doppler characteristics will not meet this characteristic, the main-sidelobe ratio comparison results of the helicopter with micro-Doppler characteristics and the strong stationary target are as follows Figure 5 If the micro-motion target point is very strong, its Doppler side lobe will also be detected by CFAR. Therefore, this embodiment obtains the target point in the following way.
[0032] According to the windowing characteristics, the main-sidelobe ratio characteristics of the Doppler dimension of the static target point are determined. According to the Doppler and distance order, the target points whose main-sidelobe ratio does not meet the Doppler dimension main-sidelobe attenuation performance of the specified window are inserted into the point trace list confirmed as the target according to the Doppler and distance order. At the same time, the target point information is added to the condensed point trace information for the subsequent helicopter identification processing of the condensed points. The hovering helicopter detection results are as follows: Figure 6 shown.
[0033] Step 4. Next, identify the target detected in steps 1-3. First, based on the distance unit information of each target point in the point trace list, intercept a small segment of raw pulse pressure data centered on the target distance unit from the original pulse pressure plane; then perform MTD processing on the intercepted data, calculate the signal-to-noise ratio of the point target, and determine whether the signal-to-noise ratio is sufficient for identification. If the signal-to-noise ratio is insufficient, no identification is performed. If the signal-to-noise ratio meets the requirements, subsequent identification processing is performed; Clean processing is performed on the pulse pressure data of the target area that meets the signal-to-noise ratio requirements to eliminate the influence of strong ground clutter, and then MTD processing is performed. At the same time, non-coherent superposition processing is performed on the second range Doppler plane of the intercepted data to improve the signal-to-noise ratio; range-dimensional CFAR processing is performed on the superimposed signal, and the maximum value of the points passing the CFAR on the range unit is counted. If the maximum value is less than the set threshold, it is not a helicopter. The main-to-side lobe ratio is judged for the points that pass the threshold, and the points that do not meet the window characteristics are judged as helicopters, otherwise they are not helicopters. Specifically, Figure 7 shown.
[0034] Step S41, obtaining the distance unit information of each target point in the point trace list, sequentially intercepting the pulse pressure data of a small range of the entire azimuth dimension with the distance unit of each target point as the center from the pulse pressure data, and performing MTD processing on the intercepted distance segment data; Step S42, according to the MTD statistics of the intercepted small segment data, the signal amplitude is determined by the amplitude of the target's range unit and Doppler unit, and the noise amplitude is determined by the area remaining after removing the clutter area and the target area. The ratio of the two is calculated as the signal-to-noise ratio of the target at the point. If the signal-to-noise ratio is less than the threshold, it is considered that the signal-to-noise ratio is insufficient and cannot be identified, and the identification process is directly jumped out. If the signal-to-noise ratio is greater than or equal to the threshold, it indicates that the target signal-to-noise ratio meets the subsequent identification requirements and can be sent to the subsequent identification process; In this embodiment, since it is necessary to detect and identify the helicopter according to the micro-Doppler effect, in order to ensure that the micro-Doppler component and the main component can be identified, it is necessary to ensure that the micro-Doppler component and the main component are both above the noise amplitude. Therefore, it is necessary to set a target signal-to-noise ratio threshold judgment. If the signal-to-noise ratio is less than the threshold, it is considered that the signal-to-noise ratio is insufficient and cannot be identified, and the identification process is directly jumped out. If the signal-to-noise ratio is greater than or equal to the threshold, it indicates that the target signal-to-noise ratio meets the subsequent identification requirements, and the helicopter target can be identified. Step S43: For the target meeting the signal-to-noise ratio requirement in step 42, the echo pulse pressure data of the target area intercepted in step 41 is processed using the Clean algorithm to remove clutter signals, and then the pulse pressure data after clutter removal is subjected to MTD processing to obtain a second range Doppler plane of the intercepted data.
[0035] Among them, in order to improve the target signal-to-noise ratio in the second range Doppler plane after the Clean processing, the second range Doppler plane in step S43 is modulo, and then the data is subjected to the front and rear Doppler channel superposition processing. In this embodiment, the second range Doppler plane of the intercepted data is processed as follows.
[0036] Step S44, dividing the second range Doppler plane of the intercepted data obtained in step S43 into a first plane and a second plane according to the Doppler channels; adding the amplitude of each point in the first plane to the amplitude of each point in the second plane to obtain a third range Doppler plane; wherein the number of Doppler channels in the first plane and the second plane is the same.
[0037] Step 5: Perform range-dimensional CFAR detection on the data on the third range Doppler plane in step S44, and the CFAR detection point traces of the plane after the MTD plane spectrum is non-coherently superimposed, such as Figure 8 As shown in the figure, the number of dots detected by CFAR on each distance unit is counted in turn, and the maximum number of dots in a single distance unit is recorded as TarDotmax. It is determined whether TarDotmax is greater than the set fixed threshold. If it is less than, it is determined to be a non-helicopter. If it is greater than or equal to the fixed threshold, it is considered to be a possible helicopter.
[0038] Step 6: For the suspected helicopter target with micro-Doppler detected in step 5, determine whether it meets the windowed main-to-sidelobe ratio feature in the plane of step 44. If it does, the target is considered to be a helicopter target with micro-Doppler, otherwise it is a non-helicopter target.
[0039] In this embodiment, when the maximum number of points on the distance unit is determined to be greater than the preset point number threshold, it is considered that the target may be a helicopter and subsequent processing is performed, otherwise the target is considered not to be a helicopter; the main-side lobe ratio of the suspected helicopter target is judged in the plane of step S44 to eliminate false alarms caused by the side lobes of strong static targets; if the main-side lobe ratio of the target does not meet the main-side lobe attenuation performance of the windowed target, the target is considered to be a helicopter, otherwise it is not a helicopter, and the recognition result is as follows: Fig. 9 shown.
[0040] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.
Claims
1. A method for detecting and identifying aerial helicopter targets based on micro-Doppler effect, characterized in that: include: Acquire radar echo pulse pressure data, perform MTD processing on the echo pulse pressure data, and obtain a first range Doppler plane; Performing CFAR detection on the points on the first range Doppler plane to obtain moving target points and slightly moving target points respectively; Obtaining the main-side lobe ratio of the micro-motion target point, inserting the micro-motion target point whose main-side lobe ratio meets the preset conditions into the moving target point to obtain the target point; Acquire a target area on a first range Doppler plane according to the target point; Perform MTD processing on the echo pulse pressure data of the target area to obtain the second range Doppler plane; Performing non-coherent superposition processing on the second range Doppler plane to obtain a third range Doppler plane, and performing CFAR detection on the points on the third range Doppler plane to obtain second CFAR detection points; When there is a distance unit in which the number of second CFAR detection traces is greater than a second preset trace number threshold in the distance unit for determining the target point, the main lobe to side lobe ratio of the target point is determined, and the target point whose main lobe to side lobe ratio meets the preset conditions is determined as the final target.
2. The method for detecting and identifying aerial helicopter targets based on micro-Doppler effect according to claim 1, characterized in that: The performing CFAR detection on the point traces on the first range Doppler plane to obtain moving target points and slightly moving target points respectively includes: Performing CFAR detection on the point traces on the first range Doppler plane to obtain first CFAR point traces; Performing point condensation on the first CFAR point trace to obtain a moving target point; Obtaining the number of point traces in each range unit except the range unit of the moving target point on the first range Doppler plane; The target distance unit is determined according to the number of point traces in each distance unit, and the micro-motion target point is obtained within the target distance unit.
3. The method for detecting and identifying aerial helicopter targets based on micro-Doppler effect according to claim 2, characterized in that: Determine the target distance unit according to the number of points in each distance unit, and obtain the micro-motion target point in the target distance unit, including: If the number of points in each of the multiple consecutive distance units is greater than the first preset point number threshold, the distance unit with the largest number of points is taken as the target distance unit; The point trace in the Doppler channel corresponding to the position with the strongest amplitude in the target distance unit is taken as the micro-motion target point.
4. The method for detecting and identifying aerial helicopter targets based on micro-Doppler effect according to claim 1, characterized in that: The target area is obtained as follows: Obtaining the entire azimuth dimension area centered on the range unit of the target point on the first range Doppler plane as a potential target area; The signal-to-noise ratio of the target point is determined, and when the signal-to-noise ratio of the target point is determined to be greater than a preset signal-to-noise ratio threshold, the potential target area is determined to be the target area.
5. The method for detecting and identifying aerial helicopter targets based on micro-Doppler effect according to claim 4 is characterized in that: The signal-to-noise ratio is determined based on the amplitude of the target point and the average amplitude of all points in the potential target area.
6. The method for detecting and identifying aerial helicopter targets based on micro-Doppler effect according to claim 1, characterized in that: The performing non-coherent superposition processing on the second range Doppler plane to obtain a third range Doppler plane comprises: The second range Doppler plane is equally divided into a first plane and a second plane according to the Doppler channel; Adding the amplitude of the point trace in each Doppler channel in the first plane to the amplitude of the point trace in the corresponding Doppler channel in the second plane to obtain a third range Doppler plane; Among them, the sum of all the two combined Doppler channel numbers is the same.
7. The method for detecting and identifying aerial helicopter targets based on micro-Doppler effect according to claim 1, characterized in that: Before performing MTD processing on the echo pulse pressure data of the target area, the method further includes: The echo pulse pressure data of the target area is processed to remove clutter.
8. The method for detecting and identifying aerial helicopter targets based on micro-Doppler effect according to claim 7, characterized in that: The echo pulse pressure data in the target area is processed with Clean algorithm to remove clutter.
9. The method for detecting and identifying aerial helicopter targets based on micro-Doppler effect according to claim 1, characterized in that: The preset condition is that the main lobe to side lobe ratio does not meet the Hamming window main lobe to side lobe attenuation performance.